# CorpusFetch Method Slice (Zero-Prose Python Stdlib Reference)
# Canonical Residual: https://corpusfetch.com/c/what-latent-state-divergence-invariants-must-survive-when-parallel-agents-share-a-base-context.md
# Remnant Warrant:    https://corpusfetch.com/c/what-latent-state-divergence-invariants-must-survive-when-parallel-agents-share-a-base-context.warrant.json
# Markdown SHA-256:   912ef83dc9ceb4a696a83a96e468c357770f07e2d2f88111833284efb7c4606e
# Code AST SHA-256:   88fd85973b293c7bd09513a37456aa8e9b3b3c333e366f97ee64956920c648b7
# Verified Classes:   DivergenceVerdict, ParallelAgent, ParallelState, DivergencePolicy, DivergenceManifest, LatentDivergenceGuard
# Defensive Guards:   0 fail-closed invariants (zero external deps)

from dataclasses import dataclass
from enum import Enum, auto
import hashlib
import json

class DivergenceVerdict(Enum):
    NO_SHARED_CONTEXT = auto()
    CONVERGED_STATE = auto()
    DIVERGENCE_ACCEPTABLE = auto()
    DIVERGENCE_CRITICAL = auto()

@dataclass(frozen=True)
class ParallelAgent:
    agent_id: str
    base_context_hash: str
    observed_updates: tuple[str, ...]
    current_state_hash: str

@dataclass(frozen=True)
class ParallelState:
    agents: tuple[ParallelAgent, ...]
    divergence_metric: float
    reconvergence_protocol: str | None

@dataclass(frozen=True)
class DivergencePolicy:
    max_acceptable_divergence: float

@dataclass(frozen=True)
class DivergenceManifest:
    verdict: DivergenceVerdict
    directive: str
    manifest_digest: str

class LatentDivergenceGuard:
    @staticmethod
    def _hash(data: str) -> str:
        return hashlib.sha256(data.encode("utf-8")).hexdigest()

    @classmethod
    def evaluate_parallelism(
        cls, state: ParallelState, policy: DivergencePolicy
    ) -> DivergenceManifest:
        
        # Gate 1: No shared context
        base_contexts = {a.base_context_hash for a in state.agents}
        if len(base_contexts) > 1:
            digest = cls._hash(json.dumps({
                "agents": str(len(state.agents)),
                "base_contexts": str(len(base_contexts))
            }, sort_keys=True))
            return DivergenceManifest(
                verdict=DivergenceVerdict.NO_SHARED_CONTEXT,
                directive=f"PROCEED: Agents have distinct base contexts, no shared-origin assumption.",
                manifest_digest=digest
            )

        # Gate 2: Identical observations (converged)
        update_sets = {frozenset(a.observed_updates) for a in state.agents}
        if len(update_sets) == 1:
            digest = cls._hash(json.dumps({
                "base": next(iter(base_contexts)),
                "updates": tuple(sorted(next(iter(update_sets))))
            }, sort_keys=True))
            return DivergenceManifest(
                verdict=DivergenceVerdict.CONVERGED_STATE,
                directive=f"PROCEED: Agents share identical update set, state converged.",
                manifest_digest=digest
            )

        # Gate 3: Divergence acceptable
        if state.divergence_metric <= policy.max_acceptable_divergence:
            digest = cls._hash(json.dumps({
                "base": next(iter(base_contexts)),
                "divergence": f"{state.divergence_metric:.3f}",
                "threshold": f"{policy.max_acceptable_divergence:.3f}"
            }, sort_keys=True))
            return DivergenceManifest(
                verdict=DivergenceVerdict.DIVERGENCE_ACCEPTABLE,
                directive=f"PROCEED: Divergence {state.divergence_metric:.3f} within threshold {policy.max_acceptable_divergence:.3f}.",
                manifest_digest=digest
            )

        # Gate 4: Critical divergence
        if not state.reconvergence_protocol:
            return DivergenceManifest(
                verdict=DivergenceVerdict.DIVERGENCE_CRITICAL,
                directive=f"HALT: Divergence {state.divergence_metric:.3f} > {policy.max_acceptable_divergence:.3f}, no reconvergence protocol.",
                manifest_digest=cls._hash(f"CRITICAL:{state.divergence_metric:.3f}")
            )

        digest = cls._hash(json.dumps({
            "base": next(iter(base_contexts)),
            "divergence": f"{state.divergence_metric:.3f}",
            "protocol": state.reconvergence_protocol
        }, sort_keys=True))
        
        return DivergenceManifest(
            verdict=DivergenceVerdict.DIVERGENCE_CRITICAL,
            directive=f"ACTION: Reconverge via {state.reconvergence_protocol}.",
            manifest_digest=digest
        )